PIPL - Practice Area Business Intelligence

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STRICTLY CONFIDENTIAL © Copyright 2014 Progressive Intelligence
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Knowledge Management
Using “Business Intelligence” for Insights
Tapping, Moulding and Utilizing Knowledge Assets
Dr. Sanjeev B. Ahuja
Managing Director
sanjeev.ahuja@piplinc.com

Overview
Background
Knowledge and its management
Practical challenges
Scope and Strategy
Compendium of KM technologies
2 STRICTLY CONFIDENTIAL © Copyright 2014 Progressive Intelligence

Background
Current state of play
Years of incorrect and misleading claims that fed a desire to “dumb down”
inherent complexities in representing and using knowledge have created a
firestorm of expectations; suppliers are over-promising and under-delivering.
Data and information sources abound in a market sans frontières
• Businesses gain competitive edge with timely and informed decision-making
− Heightened competition and market vulnerability has made intelligent assimilation
and interpretation of strategic and tactical data a mission-critical requirement
Knowledge workers in the labour force have taken on renewed import
• There is growing recognition that know-how, experience, and practices are
arguably the most significant corporate asset that must not be left tacit; it only
becomes tangible once characterised, captured, and made operative
− Differentiating low-value tasks of data capture and reporting, from high-value
sophisticated processes that generate business intelligence, requires domain
knowledge and creative data/information engineering

Background
Businesses must clearly articulate their objectives from using data, analytics, and
knowledge to make decisions, along with a strategic plan for realizing them
After wading through ubiquitous hype about the “big data” tsunami and
technology platforms for surfing it, one still cannot eke out a clear “so what” from it.
The big deal around Big Data Analysis (BDA)
• It is neither the ability to process large volumes nor the clever analytical
techniques that is the raison d’être for BDA; both are but means to an end
− Its purpose is “intelligent” or simply, informed decision making based on relevant
data, its interpretation (at times with visualization aids) to draw useful inferences,
and creation of other facts and valuable information that are otherwise “hidden”.
− Once potentially useful data sources have been identified, the next challenge is to
work one’s way through a forest of options for technology platforms, to select one
that offers a range of appropriate tools and techniques.
− It is only with aligning and integrating BDA within critical business decision
making processes that its goals come to fruition. It delivers maximal value when it
not only supports business as usual but rather, influences the way it is done.

Background
Knowledge Management is a conundrum for most businesses
Gathering correct, consistent and comprehensive knowledge is handicapped by
naïve optimism stemming from an under-estimation of the scope and complexity
of automating knowledge based decision tasks traditionally performed by people.
Knowledge
Data
Actionable
“Insights”
What is knowledge?
• Knowledge is the set of characteristics about data and information that
determine their use, role, interdependencies and usefulness
− Data are (un)structured facts (e.g., documents & content; audio & video files, text
in emails, calendars, contacts, notes, chats, messages, SMS; social network
exchanges; blogs; etc.) that are usually unorganized from a business perspective
and which provide little information regarding patterns, contexts, etc.
− Information results from applying knowledge to data by consolidating, optimizing,
categorizing, contextualizing, correlating and drawing meaningful inferences
− Knowledge is the ability to identify relevant data, recognize useful relationships
between them, understand their implications, and then apply business know-how
to use all of that for creating “insights” that the organization can act upon

Knowledge and its management
Definition of Knowledge Management (KM)
KM is the systematic management of an organization's “knowledge” assets for the
purpose of creating useful information to address its tactical and strategic goals.
“Knowledge Management” is often being used as a misnomer
• Full scope of KM is not something that is universally accepted
− The term KM is generally used to mean, making the right information available
to the right people, in the right context, to make the right decisions
− It must enable an organization to identify existing and generate new information,
quickly retrieving and using it as and when required by the business
− It is expected that both knowledge and information assets remain current and
correct, improve over time, and add to an organizations’ learning and growth
• For effective KM, an organization must develop a deep understanding of
what constitutes knowledge and information for its business and customers
− It requires a priori notions about various forms that knowledge can take, different
ways in which it can be accessed, shared, and combined, as well as when, where
and how it can be applied for generating useful information or competitive insights

Different knowledge is required for different tasks; all of it has to be managed
Knowledge may be used to narrow the context when searching for relevant data,
or to infer new facts from that data, or to present the data in a way that uncovers
information which might otherwise not be obvious, and for a range of other tasks.
Articulation of suitable knowledge artefacts is at the foundation
• Abstract facts, e.g., concepts of price, product, market and region, or of
competitors, promotion and opportunity, or world events that are important
for a business, such as wars, famines, natural catastrophes, etc.
− Organized as a network of concepts with semantic relationships between them,
into hierarchies with different levels of abstraction, or even as just a simple list
• Descriptions of know-how, e.g., data protection regulations and policies,
economic models, local laws, or even basic principles of “if-then” logic, etc.
− In some contexts, descriptions of know-how might only be considered as data, to
be further manipulated or interpreted, e.g., a list of outcomes from a Web search
on legal proceedings that involved IP infringement or patent violation, etc.

Although technical viability or computing power is becoming less of an issue, it is
still only a rare company that has successfully integrated KM into its business
strategy for supporting day-to-day decision making
Data of disparate types and formats, collected from different sources and at
different points in time, only become useful once filtered for relevance, subjected
to selective analysis and contextually interpreted.
Searching data, information and document repositories
• Google search is an obvious benchmark for keyword based retrieval of
relevant data from the Web and even the desktop
− With natural language processing functionality, auto-translation, thesauri, limited
semantic analysis, and use of “common sense” rules, Google search offers an
effective engine for searching unstructured data and document repositories
Assimilating and interpreting retrieved data, information and documents
• Google-like search is not sufficient for addressing most business needs
• A formalised knowledge base is crucial for systemic identification, generation
and interpretation of relevant, useful, and actionable insight

Once categorized and integrated data can be searched, “mined”, interpreted and
analysed, using effective presentation metaphors to communicate information
Selecting meaningful dimensions for data modelling, recognizing relevant
relationships, and knowing useful patterns on seeing them is knowledge; its
application results in information, only a small portion of it is real insight.
From data to business intelligence
• Discovery of innate relationships between data through multi-dimensional
visualization of interesting patterns is becoming increasingly critical
− Handling the exponential increase in data entering an enterprise from mobile
devices, social media, public internet, private repositories, etc., managing the
range of data types, static/dynamic, structured/unstructured, multimedia, etc.,
knowing which (if any) of that data is relevant, sharing timely information derived
from it with staff and most critically, its selective dissemination externally, enables
a business to combat competitive forces and secure a leading market position

Discovery, Categorization
and Interpretation
“Big Data” D&A
Growth and utilization
for business needs
Business Intelligence
Accurate, correct,
defensible & repeatable
Lifecycle
Management
Access, usage, and
dissemination
Governance
Information
Analytic models
and visualization
Knowledge
Domain-specific
semantics & rules
Actionable
Insights

Knowledge Systems are becoming increasingly complex
Basic capabilities of information delivery, analysis and integration now include
geospatial intelligence, complex analytics, heterogeneous data sources, hybrid
data modelling, big-data content, data discovery, and business needs of
governance, security and scalability
CAUTION: "tread with care, open mind and due consideration"
• IT-driven, user-expressed, or supplier-hyped initiatives
• Retrofitting business needs
• Unplanned lifecycle costs: development, deployment and upkeep
• Ignoring future requirements
• Doing it yourself
• Ignoring knowledge assets altogether

Scope and strategy
What are some of the key considerations for a KM system?
Several aspects can impact the value derived from KM: a) chosen application
area, b) user expectations, c) return on investment, d) practical constraints of
business environment, e) domain knowledge and f) in-house competencies.
Corporate
Perspectives
Technology
Environment
KM
Strategy
KM
Functional
Management
Organization
Culture
Organization
Processes
Information
Knowledge
Data
Insights
Implicit
Explicit

Scope and strategy
Recommended approach to establishing a knowledge competency.
Considered though not slow, staged but not overly extended, and robust as
opposed to experimental, characterises an optimal approach to maximizing value
and minimising runaway costs from strategic knowledge initiatives.
Pilot Sandbox
Goals: User Communities
Software: Open, Proprietary, Hybrid
Competencies: Internal, External
Deployment: Cloud, Hosted, Managed
Scope: Services, Operations, Infrastructure,
Technology, Location
Proofs-of-Concept
Outcomes: Insights across issue areas
Actions: Tailored to client and the problem
Processes: Repeatable, Automated
Operating Model: “Solution Centre”
Data: Internal, Client, Licensed
Security: Inside/Outside of eco-system
Environment: Dev., Staging, Production
DC: In-house, Managed, Cloud
Config: Private, Cloud (SaaS, PaaS, IaaS)
Certification: Governed, Secure, Compliant
Production

Scope and strategy
Data, knowledge, information and insight must be characterised, captured,
formalised, made operational, and preserved with the help of business experts
Knowledge can be explicit, i.e., codified in data structures, dictionaries, thesauri,
(chains of) relationships, meta-data, rules, meta-knowledge, documents, etc., or
tacit, i.e., intuitive, contextual, best practice, experience based, embedded, etc.
Technology Environment
Control Mechanisms in
Applying Knowledge
Processing for “emergent”
insights (Inferences,
Decisions, Learning)
Essential Activities for Data,
Information, and Knowledge
Management
Definition Integrating Automated/Manual Infrastructure
Type Mining On-Demand (goal driven) Applications, Platforms, Tools
Organization/Categorization Searching Spontaneous (data driven) Architecture
Analysing Storage, Backup and DR
Protection (authorization &
dissemination)
Reasoning Replication & Distribution
LC Management (create, share,
update, archive, purge)
Sourcing (internal/external) Visualizing Transportation
Handling Size/Volume Presenting Management Systems
Verification & Validation Simulation & Workflow

Scope and strategy
What must a KM Strategy address?
It must take a long-term view on knowledge management and its role in business
decision making and operations support, defining which knowledge is relevant
and which is not. Strategic investments must be made in enabling KM processes.
Performance
Management
Technology
Investments
Security,
Governance
& Policies
Operations
Processes
KM
Strategy
KM
Processes
Organization
Core
Design
Competencies
Business
Processes

Compendium of KM technologies
Analytics
KB
Decision
Support
Workflow
Simulation
Collaboration
Groupware
E-mail &
Messaging
Visualization
Technology
Environment
Databases
Access
Mechanisms
& Devices
Document
& Content
Mgmt
Extranets &
Intranets
Intelligent
Search
Social
Media
Info “Bots”
Robotic automation
with context based
search, rule based
workflows, analytics
(n-dimensional) and
knowledge based
decision support for
correct, coherent
and consistent
results.
“Emergent” Insights
Presentation, data
modelling, graphical
metaphors and
interactive functions
for visualization and
discovery.
Data/Info Stores
Platforms for
collaboration and
groupware, social
media, messaging,
e-mails, documents,
multimedia content,
data/info sources.
Data Flow & Search
Middleware for
device independent
access over public,
shared and private
networks, with
intelligent search
engines to hone-in
on what is relevant.

Progressive Intelligence, Ltd.
Trusted Advice, Hands-on Experience, Practical Application
PI Consulting Group (PICG) partners bring over 30 years of strategic problem
solving and implementation experience, with independent advisory, programme
governance and leadership development services in business operations,
Telecoms & IT, information and knowledge management.
Credentials - Dr. Sanjeev B. Ahuja
• CxO tenures at start-up/early-stage, mid-size, and large-scale global firms
− Managing Partner of PICG, a strategy and operations management consultancy
offering advisory, technology and delivery services with 18 senior professionals
− Founder, President & CEO of a € 35M firm with 150 staff delivering technology
solutions and professional services in CRM and Business Intelligence
− Global CIO & VP Business Ops. of a $4.3M mobile satellite communications
company, managing its global billing and customer care operations, strategic
partnerships and P&L responsibility for a shared services centre
− Prof. and Director of Graduate Studies at University of Maryland (USA); author of
numerous articles and served on programme committees of Int’l conferences
− PhD (1985) & MS (1981) - Artificial Intelligence; BSc (1978) - Elec. Engineering

Progressive Intelligence, Ltd.

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